The semiconductor device such as Large Scale Integrated (LSI) circuits is tested at the shipping after the design and manufacturing. When any failure is detected at the shipping test and in the market, a failure analysis using a logical simulation or failure dictionary is carried out to extract failure candidates. Based on the failure candidates, failure factors are narrowed by the volume diagnosis in which the statistical analysis is carried out. Then, a failure candidate associated with the narrowed failure factors is selected to determine whether or not the selected failure candidate actually corresponds to a failure on the semiconductor device by a physical analysis using the electron microscope or the like, and a failure cause is identified. The failure cause is feedback to the design of the semiconductor device and/or manufacturing procedure, and any change is provided to decrease the number of failures detected at the shipping test and the like.
The failure diagnosis is a technique to presume a failure location inside of the semiconductor device for which a failure is detected by the shipping test or the like after the manufacturing. Recently, a method is proposed to further narrow the failure factors and/or to presume the failure location by using statistical analysis in the volume diagnosis.
On the other hand, the cost of the physical analysis increases along with sophisticating the manufacturing processes and enlarging the scale of the circuit. In order to decrease the cost of the physical analysis, and early identify the failure cause, it is preferable that the failure candidates to be analysis by the physical analysis are appropriately narrowed in the volume diagnosis.
Conventionally, a method of the volume diagnosis is proposed, where the statistical analysis is carried out based on failure reports of the semiconductor device, which are inputted from the failure analysis tool to output a feature that is a failure factor according to contribution degrees to the failure. The failure report includes information concerning nets or input/output pins as the failure candidates, and may further include a type of failure, such as open failure or bridge failure. Typically, in the method of the volume diagnosis, a list of the features as the candidates of the failure factors is inputted or embodied in a diagnosis apparatus in advance. Here, the features that are the failure factors include layout information such as wiring length, the number of via holes, and wiring density, wiring pattern, which is a factor of the open failure or bridge failure, and the like. This proposed method of the volume diagnosis pays attention to one certain type of feature, and uniformly classifies the circuit information such as netlists to plural groups from the top by sorting in descending order of the feature value of the feature to which the attention is paid. For each group, an expected value of the number of failures and measured value of the number of failures are respectively calculated. The expected value is calculated using a model expression based on the feature value of the feature to which the attention is paid, and the measured value is calculated by counting the failure candidates included in each group from the failure list. In addition, the contribution degree (or importance degree) of the one certain type of feature, to which the attention is paid, to the failure is calculated based on the similarity of the distributions of the expected value and measured value. By repeating the aforementioned processing for all types of features to calculate the contribution degrees of the respective types of features to the failure, the feature of the type whose contribution degree is high is identified as the failure factor.
However, the main object of this technique is to shorten the time required for identifying the failure factor. In addition, even when the ranking of the features by the contribution degree and information concerning the failure location are obtained, it is difficult to specifically determine how much the design should be modified. For example, when the failure rate becomes high in an area where the via holes concentrate, it is difficult for the designer to determine how much the density of the via holes should be lowered. In addition, unless the difficulty of the modification is considered, it is impossible to appropriately determine for which feature the design should be modified.
Namely, there is no indicator appropriate for the design change to be carried out in order to decrease the failure of the semiconductor device.